Social networking has become an increasingly popular presence on the Internet. Social network services allow users to easily connect with friends, family members, and other users in order to share, among other things, comments regarding activities, interests, and other thoughts. As social networking has continued to grow, organizations have recognized its value. For instance, companies have found that social networking provides a great tool for managing their brand and driving consumers to their own web sites or to otherwise purchase their products or services. Companies can create their own social networking profiles for communicating with consumers via social networking posts and other messages. Additionally, since users often employ social networking to comment on products and services, companies can mine social data to identify what consumers are saying about them, as well as their products, services, and industry in general.
Identifying trending topics, particularly in real-time, within a social network environment can be difficult due to the extensive amount of available content. In particular, processing such large amounts of data can be both time and computationally intensive. Further, traditional topic extraction methods assign data to a pre-determined set of topics which is not effective for the rapidly changing and unpredictable content typical in social media. Traditional topic extraction methods assign data to a pre-determined set of topics which is not effective for the rapidly changing and often unpredictable content in social media. Consequently, traditional topic extraction methods result in inaccurate or outdated trends being identified to users. Still further, because social media is often noisy, discovering meaningful topics and determining when a term actually has meaning is a challenge.